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It is typically small in normal eyes but increases with neuroretinal rim loss 4. The optic cup is the distinguishable excavation in the central portion of the ONH. Neuroretinal rim thinning can be quantified in fundus photos by measuring the vertical cup-to-disc ratio (VCDR) 3. During clinical examination and optic disc photo analysis, ophthalmologists evaluate the ONH, looking for typical changes such as generalized or focal neural rim thinning. Glaucoma is associated with characteristic changes of the optic nerve head (ONH), also called the optic disc 2. This chronic neuropathy induces structural optic nerve fiber damage with visible changes in and outside the optic disc, ultimately leading to functional vision loss. Glaucoma is a leading cause of irreversible blindness in our ageing society with a projected number of patients of 112 million by 2040 1. Our findings provide the first irrefutable evidence that deep learning can detect glaucoma from fundus image regions outside the ONH. Models that were trained on images with absence of the ONH are still able to obtain significant performance (0.88 AUC for glaucoma detection and 37% R 2 score for VCDR estimation in the most extreme setup of 60% ONH crop). Trained models using original images resulted in an area under the curve (AUC) of 0.94 for glaucoma detection, and a coefficient of determination (R 2) equal to 77% for VCDR estimation. The inverse of the cropping mask was also applied (periphery crop policy). We defined the crop radius as a percentage of image size, centered on the optic nerve head (ONH), with an equidistant spaced range from 10–60% (ONH crop policy). We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy.

Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency.